Methods and applications in the analysis of social data in healthcare

  in Special Issue   Posted on June 12, 2019

Information for the Special Issue

Special Issue Call for Papers:

Topics and scope of the proposed special issue

The topics to be covered include, but are not limited to:

• Challenges in social data analytics in healthcare:

o i) data management

o ii) data curation

o iii) opinion mining and sentiment analysis

o iv) privacy-aware data mining algorithms

o v) data quality and veracity

o vi) natural language processing and text mining

o vii) semantics

o viii) trends in discovery and analysis

o ix) graph mining and community detection

o x) social sensors

o xi) IoT devices

• Applications in social data analytics in healthcare:

o i) epidemiological analysis

o ii) outbreak detection

o iii) human behavior

o iv) medical skills and education

o v) personalized medicine

o vi) diagnosis, prognosis and prognostics

Target authors and contributors

The special issue is mainly oriented to the authors that have accepted papers submitted to the IEEE International Symposium on Computer-Based Medical Systems (CBMS 2019).

However, other authors which have not participated in the conference can also submit their papers.

Review procedure

The review will be done by at least two reviewers using a double-blind peer review process.

In the case that disagreement between both reviewers take place, a third reviewer or one of the editors will review the paper to make a decision.

Important dates

Since the CBMS conference will take place on June 2019 our tentative schedule will be the following:

• Manuscript submission: October 30th, 2019.

• Author notification: December 15th, 2019.

• Special issue publication: About January 2020.

Guest editors

Alejandro Rodríguez-González, Universidad Politécnica de Madrid, Spain.

Sebastian Ventura Soto, Universidad de Córdoba, Spain.

Paolo Soda, Università Campus Bio-Medico di Roma, Italy.

Jesualdo Tomás Fernández-Breis, Universidad de Murcia, IMIB- Arrixaca, Spain.


Typical references related to the special issue topics

Altug Akay, Andrei Dragomir, & Bjorn-Erik Erlandsson. (2015). Mining Social Media Big Data for Health. Retrieved from

Asamoah, D., Sharda, R., & Kumarasamy, A. T. (2015). Can Social Media Support Public Health? Demonstrating Disease Surveillance using Big Data Analytics. AMCIS 2015 Proceedings. Retrieved from

Evika Karamagioli. (2015). Social media as a big public health data source: review of the international bibliography. PeerJ Preprint.

Kambatla, K., Kollias, G., Kumar, V., & Grama, A. (2014). Trends in big data analytics. Journal of Parallel and Distributed Computing, 74(7), 2561–2573.

Kamel Boulos, M. N., Sanfilippo, A. P., Corley, C. D., & Wheeler, S. (2010). Social Web mining and exploitation for serious applications: Technosocial Predictive Analytics and related technologies for public health, environmental and national security surveillance. Computer Methods and Programs in Biomedicine, 100(1), 16–23.

Kang, M., Zhong, H., He, J., Rutherford, S., & Yang, F. (2013). Using Google Trends for Influenza Surveillance in South China. PLOS ONE, 8(1), e55205.

Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014). The Parable of Google Flu: Traps in Big Data Analysis. Science, 343(6176), 1203–1205.

Lee, K., Agrawal, A., & Choudhary, A. (2013). Real-time Disease Surveillance Using Twitter Data: Demonstration on Flu and Cancer. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1474–1477). New York, NY, USA: ACM.

Rodríguez-González, A., Mayer, M. A., & Fernández-Breis, J. T. (2013). Biomedical information through the implementation of social media environments. Journal of Biomedical Informatics, 46(6), 955–956.

Rodríguez-González, A., Ruiz, E. M., & Mayer , M. A. (2016). Automatic extraction and identification of users’ responses in Facebook medical quizzes. Computer Methods and Programs in Biomedicine, 127, 197–203.

Sarker, A., Ginn, R., Nikfarjam, A., O’Connor, K., Smith, K., Jayaraman, S., Gonzalez, G. (2015). Utilizing social media data for pharmacovigilance: A review. Journal of Biomedical Informatics, 54, 202–212.

Schneeweiss, S. (2014). Learning from Big Health Care Data. New England Journal of Medicine, 370(23), 2161–2163.

Signorini, A., Segre, A. M., & Polgreen, P. M. (2011). The Use of Twitter to Track Levels of Disease Activity and Public Concern in the U.S. during the Influenza A H1N1 Pandemic. PLOS ONE, 6(5), e19467.

Social networks and big data meet public health | WWW 2013 – Rio de Janeiro, Brazil. (2013). Retrieved December 19, 2016, from /2013/04/25/social-networks-and-big-data-meet-publichealth/

Stephanie Reardon. (2015, February 2). Social Media Big Data Mining May Improve Population Health. Retrieved December 19, 2016, from

Wang, F., Wang, H., Xu, K., Raymond, R., Chon, J., Fuller, S., & Debruyn, A. (2016). Regional Level Influenza Study with Geo-Tagged Twitter Data. Journal of Medical Systems, 40(8), 189.

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